Papers with Coreference resolution
French Coreference for Spoken and Written Language (2020.lrec-1)
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| Challenge: | In French, most coreference resolution systems run different setups, making comparisons difficult. |
| Approach: | They present a full-stack model that outperforms other approaches for coreference resolution in French . they compare it with the first end-to-end neural French coreference model trained on democrat . |
| Outcome: | The proposed model outperforms the current systems for spoken and written French. |
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering (P18-2)
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| Challenge: | Existing methods for identifying and clustering mentions in text are complex and require heuristics to solve. |
| Approach: | They propose to use a biaffine attention model to get antecedent scores for each possible mention and optimize mention detection and mention clustering accuracy given the mention cluster labels. |
| Outcome: | The proposed model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set. |
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers (D18-1)
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| Challenge: | Recent coreference resolvers have notable improvements on the CoNLL evaluation sets, but struggle to generalize properly to new datasets. |
| Approach: | They investigate the role of linguistic features in building more generalizable coreference resolvers . they show that employing features and subsets of their values that are informative for coreference resolution improves generalization . |
| Outcome: | The proposed system achieves state-of-the-art results on WikiCoref, compared with a system trained on CoNLL. |
Joint Coreference Resolution and Character Linking for Multiparty Conversation (2021.eacl-main)
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| Challenge: | Character linking is the task of linking mentioned people in conversations to the real world . human use of pronouns or normal entities makes it difficult to link mentioned people to real people . a critical step towards understanding conversations is grounding mentioned people - a goal of the natural language processing community . |
| Approach: | They propose to integrate richer context from the coreference relations among different mentions to help the linking task. |
| Outcome: | The proposed model outperforms all previous models on both tasks. |
Sanaphor++: Combining Deep Neural Networks with Semantics for Coreference Resolution (L18-1)
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Julien Plu, Roman Prokofyev, Alberto Tonon, Philippe Cudré-Mauroux, Djellel Eddine Difallah, Raphaël Troncy, Giuseppe Rizzo
| Challenge: | Coreference resolution is a challenging task in Natural Language Processing . since a few years, the biggest step forward has been made using deep neural networks . |
| Approach: | They propose to improve coreference resolution by adding semantic features to a top-level deep neural network system . they evaluate a shared task dataset and compare it to the state-of-the-art system based on Stanford deep-coref . |
| Outcome: | The proposed system achieves 1.13% gain over the CoNLL 2012 dataset and the state-of-the-art system. |
Parallel Data Helps Neural Entity Coreference Resolution (2023.findings-acl)
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| Challenge: | Current neural coreference models are trained on monolingual annotated data but annotating such coreference information is expensive and challenging. |
| Approach: | They propose a simple yet effective model to exploit coreference knowledge from parallel data. |
| Outcome: | The proposed model improves on the OntoNotes 5.0 English dataset by 1.74 percentage points . it is based on an unsupervised module learning coreference from annotations . |
Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution (2022.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) is a way to preserve the semantic meaning of a sentence in a graph. |
| Approach: | They propose a general pretraining method that leverages any general AMR corpus and even automatically parses AMR data to achieve performance gains of up to 6% absolute F1 points. |
| Outcome: | The proposed model significantly improves on the previous state-of-the-art model by up to 11% F1. |
Adapting Coreference Resolution for Processing Violent Death Narratives (2021.naacl-main)
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| Challenge: | Existing coreference models suffer from poortransferability due to domain gaps . existing models are not robust enough to handle text data about LGBT individuals . |
| Approach: | They propose to use a dataaugmentation rule to improve coreference resolution in an administrative database written in English to better handle LGBT data. |
| Outcome: | The proposed model improves perfor-mance and accuracy of coreference resolution in a violent death nar-rative from the Centers for Disease Control's (CDC) national Violent Death Re-porting System. |
Signed Coreference Resolution (2021.emnlp-main)
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| Challenge: | Sign Language Processing is based on linguistic theories of spoken languages and expect either speech or written text as input. |
| Approach: | They propose a new challenge for coreference modeling and Sign Language Processing to solve this problem. |
| Outcome: | The proposed models will be linguistically informed and can address the complexities of the challenge effectively. |
Coreference Reasoning in Machine Reading Comprehension (2021.acl-long)
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| Challenge: | Existing datasets for machine reading comprehension do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Approach: | They propose to use existing coreference resolution datasets to train machine reading comprehension models to better reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Outcome: | The proposed method improves the performance of state-of-the-art models on a set of coreference-related datasets. |
Cross-document Coreference Resolution over Predicted Mentions (2021.findings-acl)
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| Challenge: | Cross-document coreference resolution has been under-explored in recent years . however, the challenge of cross-document resolution remains relatively under-studied . |
| Approach: | They propose a model for cross-document coreference resolution from raw text that extends a prominent withindocument corefer model to the CD setting. |
| Outcome: | The proposed model achieves competitive results for event and entity coreference resolution on gold mentions. |
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
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| Challenge: | Existing studies of natural language labelling tasks have shown that crowd-sourced labels can be noisy. |
| Approach: | They split the aggregation into mention classification and coreference chain inference tasks to predict the correct labels. |
| Outcome: | The proposed model predicts the class of each mention using an autoencoder while taking into account the mention’s annotation complexity and annotators’ reliability at different levels. |
Multilingual Coreference Resolution in Low-resource South Asian Languages (2024.lrec-main)
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| Challenge: | Existing coreference resolution models for South Asian languages are limited . a a sanity check for the prediction of translations is required to ensure accuracy of the model, authors say . |
| Approach: | They evaluate an end-to-end coreference resolution model on a Hindi golden set . they use translation and word-alignment tools to translate a translated dataset into 31 languages . |
| Outcome: | The proposed model scored 64 and 68 on a Hindi golden set. |
text2story: A Python Toolkit to Extract and Visualize Story Components of Narrative Text (2024.lrec-main)
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| Challenge: | Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law. |
| Approach: | They propose to use an array of narrative extraction tools to extract narratives from text . the package contains an array and an experimental module for evaluation . |
| Outcome: | The text2story python supports the narrative extraction and visualization pipeline. |
Interpretable Coreference Resolution Evaluation Using Explicit Semantics (2026.acl-long)
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| Challenge: | Existing evaluation methods for coreference resolution are limited by semantic and contextual information. |
| Approach: | They propose a semantically-enhanced evaluation framework for coreference resolution that assigns semantic labels to nominal mentions and propagates them to entire coreference clusters. |
| Outcome: | The proposed framework uncovers systematic weaknesses obscured by standard metrics. |